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Prd V08 Drift Baseline Compare

Community
mattgierhart
prd-v08-drift-baseline-compare

Establish baseline → snapshot → compare → history monitoring for any KPI, config, or metric that can drift during PRD v0.8 Deployment & Ops. Triggers on requests to monitor drift, baseline a value for later comparison, or when user asks "how do we track if X changes?", "baseline this", "config drift", "performance regression", "metric drift", "compare to last week", "is this getting worse?". Outputs MON-DRIFT-* entries with baseline + comparison rules.

Overview

Publishermattgierhart
RepositoryPRD-driven-context-engineering
Skill nameprd-v08-drift-baseline-compare
Stars
179
Forks
11
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mattgierhart on GitHub. Read the source before you install it.

Installation

Install the Prd V08 Drift Baseline Compare AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mattgierhart/PRD-driven-context-engineering.git /tmp/PRD-driven-context-engineering
mkdir -p .claude/skills
cp -r /tmp/PRD-driven-context-engineering/plugins/prd-ce/skills/prd-v08-drift-baseline-compare .claude/skills/prd-v08-drift-baseline-compare
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd V08 Drift Baseline Compare in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prd V08 Drift Baseline Compare on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prd V08 Drift Baseline Compare is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Drift: Baseline / Compare / History

Position in workflow: v0.8 Monitoring Setup → v0.8 Drift: Baseline / Compare / History → v0.8 Runbook Creation

Execution Mode

Default is standard. See .claude/rules/08-skill-execution-modes.md for selection logic.

ModeWhat this skill produces
quickOne metric / config / dataset baselined; weekly compare schedule; simple threshold alert
standard3–5 things monitored; baseline + tiered thresholds (warn / critical); compare cadence; history retention
deepFull portfolio; multi-dimensional comparison (per segment, per environment); regression-cause matrix; auto-baselining after intentional change

What This Does

Generalizes a pattern AgriciDaniel's claude-seo skill encodes for SEO drift — baseline → snapshot → compare → history — into a reusable monitoring shape that works for any metric, config, or dataset that can change over time and needs to be watched.

This is drift monitoring, distinct from alerting on absolute thresholds. Alerting answers "is X over the line right now?" Drift monitoring answers "is X different from last week's normal?" — which catches slow regressions that absolute thresholds miss.

Examples of things worth drift-monitoring:

  • KPI: activation rate week-over-week
  • AI search position: ChatGPT/Perplexity ranking for target queries
  • Config: feature-flag rollout percentages
  • Performance: p95 latency by endpoint
  • Cost: per-user infra cost
  • Marketing: per-channel CAC trend
  • Content: changelog post engagement
  • Third-party: vendor pricing pages (price hikes), competitor feature pages (parity loss)

How It Works

  1. Pick what to monitor — One thing per MON-DRIFT- entry. Must be:
    • Quantifiable (number, percentage, list, configuration value)
    • Snapshotable (captureable at a point in time, ideally automatically)
    • Causally interpretable (when it changes, you know enough to investigate)
  2. Capture the baseline — Take a snapshot. Date it. Store in version control or a known location (status/baselines/, monitoring/snapshots/, etc.).
  3. Define drift thresholds:
    • Warn: meaningful change (e.g., 10% drift in a KPI; any change in a config value)
    • Critical: serious change (e.g., 25% KPI drop; breaking config change)
    • Recalibrate: intentional change that should refresh the baseline (e.g., after a feature rollout, the baseline is wrong; refresh it)
  4. Set compare cadence — How often does this get re-snapshotted?
    • Hot (hourly/daily): production KPIs, AI search positions during a launch
    • Warm (weekly): standard product KPIs, content engagement
    • Cool (monthly): vendor pricing, competitor feature parity, infra cost
  5. Build the compare procedure — A script or runbook that:
    • Takes a new snapshot
    • Diffs against baseline
    • Computes drift % per dimension
    • Emits warn/critical signals at thresholds
    • Appends to history log
  6. Plan auto-baselining after intentional change [standard+] — When the team makes a deliberate change (ships a feature that should improve activation), the old baseline becomes wrong. Define what triggers a baseline refresh and who approves it.

Example

Monitoring AI search position for the target query "best CRO tool for SaaS founders" across 3 surfaces. (Generalized from AEO Audit re-test cadence.)

Baseline (2026-04-01):

ChatGPT: not mentioned (rank: --)
Perplexity: rank 4 of 5, miscategorized as "analytics"
AI Overviews: not mentioned
Sources cited: [competitor1.com, competitor2.com, reddit.com/r/SaaS]

Thresholds:

  • Warn: rank changes by ≥1, sources list changes, miscategorization persists
  • Critical: dropped from any surface where previously mentioned

Compare cadence: weekly during launch month, monthly after.

Re-snapshot (2026-04-15):

ChatGPT: rank 3 of 5 (NEW)         ← improved
Perplexity: rank 3 of 5, category corrected (NEW)   ← improved
AI Overviews: rank 5 of 7 (NEW)    ← improved
Sources cited: [+ ourdomain.com (NEW), ...]

Compare output: 3 surfaces improved. Likely cause: alternatives-pages campaign + new CRO-anchored blog post indexed.

History append: 2026-04-01 → 2026-04-15 row added to history log.

Action: No alert; positive drift. Baseline can stay (next compare in 2 weeks). If the trend reverses, the baseline catches it.

What You Get Back

  • MON-DRIFT-* entries (one per monitored thing) — Baseline snapshot, thresholds, cadence, compare procedure, history pointer
  • History logs in status/drift-history/<id>.jsonl (or similar) — Append-only record of compare results
  • Compare scripts (scripts/drift/<id>.sh or equivalent) when automatable

When to Use It

TriggerMode
Post-launch — need ongoing watch on KPIsstandard
AEO Audit results to maintainstandard
Competitor feature parity trackingstandard
Vendor / third-party pricing watchquick
Performance regression huntingdeep
Infrastructure cost trendquick

Do not use for things that are already covered by threshold-based alerting (RED/USE metrics, MON-* alerts). Drift is for trends; alerting is for absolutes.

Consumes

  • MON-* monitoring infrastructure (from v0.8 Monitoring Setup) — Data sources for KPI/performance drift; existing dashboards
  • KPI-* metric definitions (from v0.3 + v0.9) — What's worth watching
  • CFD-* competitor research (from v0.2 + ongoing) — For competitor-parity drift
  • GTM-AEO-* Coverage Matrix (from v0.9 AEO Audit) — For AI search position drift
  • BR-PRICING-* (from v0.3 + v0.9 Offer) — For pricing drift on our side; vendor-pricing drift watches their side
  • DEP-* release entries — Intentional changes that may trigger baseline refresh

Produces

  • MON-DRIFT-* entries with Type=Drift-Baseline — One per monitored thing
  • History logs in status/drift-history/ — Append-only diff history
  • Compare scripts in scripts/drift/ (when automatable)
  • Baseline-refresh approvals — Recorded when an intentional change resets a baseline

Output Template

MON-DRIFT-XXX: Baseline — [What is being monitored]
Type: Drift-Baseline
Category: [KPI | AI-Search | Config | Performance | Cost | Vendor | Competitor]
Owner: [Person / role]
Status: Active

Subject: [Specific thing — e.g., "ChatGPT rank for query 'best CRO tool for SaaS'"]

Baseline (YYYY-MM-DD):
  [Snapshot — values, list, config dump, etc.]

Thresholds:
  Warn: [Condition — e.g., "rank change ≥1 OR sources list changes"]
  Critical: [Condition — e.g., "dropped from any surface previously mentioned"]
  Recalibrate-trigger: [What intentional change resets the baseline]

Compare cadence: [Hourly | Daily | Weekly | Monthly | Triggered]
Compare procedure: [Script path OR manual runbook reference]

History: status/drift-history/<id>.jsonl

Re-snapshot history:
  - YYYY-MM-DD: [summary of last compare result]
  - YYYY-MM-DD: ...

Linked IDs: MON-XXX (data source), KPI-YYY or CFD-ZZZ or GTM-AEO-AAA (subject)

Anti-Patterns

PatternSignalFix
Stale baselineComparing today's value against a 12-month-old baseline that no longer reflects "normal"Add recalibrate-trigger conditions; refresh after intentional changes
Drift alerting on noiseCritical alert fires every week for 2% movementThreshold is too tight; widen warn, tighten critical
No history logSnapshot, compare, alert, forgetAppend-only history is the whole point; without it, you can't see trends
Monitoring everything50 MON-DRIFT-* entries, nobody reads anyPick the 3–5 things that matter; archive the rest
No causal interpretation"Drift detected" with no investigation pathEach drift signal should map to candidate causes (recent release, competitor move, AI surface change)
Drift instead of alertingUsing drift for things that need absolute thresholdsDrift = trends; absolutes (latency > 500ms) = alerting

Quality Gates

Before activating drift monitoring:

  • Subject is quantifiable and snapshotable
  • Baseline captured with explicit date
  • Warn and critical thresholds defined
  • Compare cadence committed
  • Compare procedure (script or runbook) exists
  • History log path defined and writable
  • Recalibrate-trigger documented (when does baseline get refreshed?)
  • Owner assigned

Downstream Connections

ConsumerWhat it usesExample
Runbook Creation (RUN-)Drift criticals trigger investigation runbooksMON-DRIFT-AEO critical → RUN-investigate-aeo-loss
Launch MetricsDrift on launch KPIs informs go/no-go decisionsKPI-activation drift ≥10% → re-look at GTM
Feedback Loop SetupDrift in customer-facing metrics drives CFD- investigationActivation drift down → CFD- interview cohort
v0.9 AEO Audit (re-run)Drift in AI search positions triggers re-auditAEO drift → re-run aeo-audit
v1.0 Continuous DiscoveryDrift patterns inform discovery questions"Why is X drifting?" → Teresa Torres discovery

Detailed References

  • AgriciDaniel's seo-drift skill — original baseline/compare/history pattern for SEO
  • Site Reliability Engineering (Google) — SLO-burn-rate alerting (related but different)
  • (No bundled references/ — pattern is reusable; the content lives in each MON-DRIFT- entry)

Frequently asked questions

What does the Prd V08 Drift Baseline Compare AI skill do?

Establish baseline → snapshot → compare → history monitoring for any KPI, config, or metric that can drift during PRD v0.8 Deployment & Ops. Triggers on requests to monitor drift, baseline a value for later comparison, or when user asks "how do we track if X changes?", "baseline this", "config drift", "performance regression", "metric drift", "compare to last week", "is this getting worse?". Outputs MON-DRIFT-* entries with baseline + comparison rules.

Why use Prd V08 Drift Baseline Compare on TypingMind?

Because you install it once and use it with any model. Prd V08 Drift Baseline Compare is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prd V08 Drift Baseline Compare in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mattgierhart/PRD-driven-context-engineering/tree/main/plugins/prd-ce/skills/prd-v08-drift-baseline-compare. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prd V08 Drift Baseline Compare?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prd V08 Drift Baseline Compare?

As many as you like. As long as a model supports skills, you can use Prd V08 Drift Baseline Compare with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prd V08 Drift Baseline Compare AI skill free?

Yes. It is published on GitHub by mattgierhart under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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